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5 min read
This article was originally published by Danish Industry (DI) on February 25, 2025:
Source (in Danish).
One of America's leading helicopter manufacturers faced significant challenges in minimizing helicopter downtime and enhancing operational readiness. Traditionally, helicopter maintenance and repair involved manual processes and long chains of communication that relied heavily on individual human expertise.
This process required numerous manual evaluations, where technicians had to review similar past damages, analyze images of the current damage, and develop repair proposals based on previous experiences. This created a risk for human error and inefficiency, especially since access to experience-based knowledge was limited and the images could be of poor quality, resulting in prolonged communication.
With a desire to optimize these processes and improve efficiency, the helicopter manufacturer explored a solution that combined artificial intelligence with traditional machine learning. They turned to software company Empact, based in Norway, which developed this solution.
3 Quick Facts about Empact
- Empact develops digital solutions for businesses.
- Empact was founded in 2006 with locations in Denmark, Norway, Romania, and the US.
- Empact is committed to transforming the way organizations leverage digital technology, including AI.
'The solution has automated a manual dialogue that previously involved extensive follow-up work and historical data analysis.' Jacob Gliemann Larsen, Director of Defense, Empact.
AI Assistant Boosts Helicopter Repair
Empact's AI solution functions as an intelligent assistant—essentially a chatbot—for technicians and maintenance teams working on helicopter repair and maintenance. By leveraging traditional machine learning, enhanced with an AI model trained on an extensive dataset of past damages and repairs, the assistant quickly identifies the type of damage and suggests a relevant repair method.
Jacob Gliemann Larsen, Director of Defense at Empact, explains:
'We have automated a manual dialogue and replaced it with an AI model that analyzes images and data from previous damages. Now, technicians can receive repair suggestions faster and with greater accuracy.'
The process is facilitated through a chat-based system, similar to ChatGPT or Claude, which is integrated into the customer's maintenance system. When a mechanic detects damage, the AI automatically analyzes the images and compares them to similar previous cases. Within a short time, it suggests the most suitable repair method, which is then validated and approved by a human expert.
By shifting the dialogue from a traditional customer care center to a direct interaction between the mechanic and the AI chatbot, the solution delivers faster responses and higher quality answers that no longer depend solely on the expertise of individual customer service agents. Ultimately, this results in helicopters achieving a higher degree of uptime. As Jacob explains:
'The dialogue used to be very lengthy, and when that drags on, the helicopter remains grounded and cannot fly.'
He continues:
'I would estimate that a helicopter going through this process gains at least an extra two weeks of uptime compared to before.'
A visualization of the process is shown below.
The Importance and Challenge—a Strong Data Foundation
For other companies considering AI solutions, Empact emphasizes the importance of a robust data foundation. One challenge has been the relatively small number of recorded helicopter damages, meaning the model must be continuously fine-tuned with a somewhat thin dataset. However, accuracy is expected to improve over time as more data becomes available.
Jacob Gliemann Larsen sums it up:
'It is not like there are a million cases—thankfully. But this does raise questions about whether the data foundation is sufficient. There should be enough, but we cannot be certain yet.'
While this solution was designed for helicopter maintenance, AI-assisted troubleshooting has the potential to transform any industry that relies on fast and accurate damage assessments—from factories aiming to minimize machine downtime to logistics companies ensuring fleet stability. The key is using data and AI in a smarter way to optimize maintenance processes.
Read on to discover our best tips for success.
3 Tips for Success
- Build on a Solid Data Foundation
AI works best when trained on reliable and relevant data. Ensure your data is comprehensive and of high quality to achieve accurate and actionable results. - Integrate AI as Support, Not a Replacement
Use AI as a tool to support human expertise rather than as a complete replacement. While AI can automate tasks and provide insights, human judgment remains crucial—as demonstrated by Empact's solution. - Prepare for an Iterative Process
AI solutions evolve over time. Be patient and expect ongoing optimization. Each iteration brings improvements, and AI becomes more intelligent as it receives additional data and feedback.
Meet Jacob
Jacob has worked in the software industry since 1991. His greatest passion is business development, which has led him on a journey through small, medium, and large companies—all focused on cutting-edge technology and innovation.
In his current role, he promotes the digitization of production and maintenance, helping defense companies and operators maximize their potential through digital solutions.
Reach out to Jacob on LinkedIn.